Testing the agriculture-induced EKC hypothesis: the case of Pakistan

Abstract

This study investigates the long-run equilibrium relationship among carbon dioxide (CO2) emissions, income growth, energy consumption, and agriculture, thus testing the existence of what we call the agriculture-induced environmental Kuznets’ curve (EKC) hypothesis in the case of Pakistan for the period of 1971–2014. The long-run equilibrium relationship among the variables in the conducted model is confirmed by Maki’s (EM 29(5), 2011–2015, 2012) co-integration test under multiple structural breaks. Toda-Yamamoto’s (JE 66(1):225–250, 1995) causality test results reveal bidirectional causal relationships among gross domestic product (GDP), energy use, agriculture, and CO2 emissions. Fully modified ordinary least squares (FMOLS) results suggest that GDP has elastic positive impacts on CO2 emissions, and energy use and agricultural value added have inelastic positive impacts on CO2 emissions, whereas squared GDP has an inelastic and negative effect on CO2 emissions. This finding confirms the existence of the agriculture-induced EKC hypothesis in Pakistan and can be a guideline for other agrarian developing countries for the creation of effective policies around environmental degradation.

τT represents the most general model with a drift and trend; τμ is the model with a drift and without trend; τ is the most restricted model without a drift and trend. The numbers in brackets are lag lengths used in ADF test to remove serial correlation in the residuals. When using PP test, the numbers in brackets represent the Newey-West bandwidth (as determined by Bartlett-Kernel). Both in ADF and PP tests, unit root tests were performed from the most general to the least specific model by eliminating trend and intercept across the models. *Rejection of the null hypothesis at the 1% level of significance. Tests for unit roots have been carried out in E-VIEWS 10.0

Table 7

Johansen co-integration test results

Hypothesized no. of CE(s)

Eigenvalue

Trace statistic

5% critical value

1% critical value

None**

0.716162

112.1147

68.52

76.07

At most 1**

0.617625

57.96249

47.21

54.46

At most 2

0.225592

16.62428

29.68

35.65

At most 3

0.122442

5.631063

15.41

20.04

At most 4

0.000343

0.014743

3.76

6.65

Trace test indicates two co-integrating equation(s) at both 5 and 1% levels